In real clinical applications, the uncertainty of predictions is important in OARs segmentation. For radiotherapy planning, radiologists care about not only how accurate the segmentation results are, but also the extent to which the model is confident about its predictions. If the uncertainty is too high, then doctors need to focus on this part of area and may give refinements [1]}. In our case, the low contrast between OARs and their surroundings leads the voxels around boundaries to be segmented with less confidence [2]}. The uncertainty information of these voxels can indicate regions that have potentially been mis-segmented, and could be used to guide human to refine the prediction results [3]}, [4]}. However, to the best of our knowledge, there have been no existing works exploring the uncertainty estimation in HAN OARs predictions and its relationship with mis-segmentation.
